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Efficiency Measurement PowerPoint PPT Presentation


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William Greene Stern School of Business New York University. Efficiency Measurement. Lab Session 2. Stochastic Frontier Estimation. Application to Spanish Dairy Farms. N = 247 farms, T = 6 years (1993-1998). Using Farm Means of the Data. OLS vs. Frontier/MLE. JLMS Inefficiency Estimator.

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Efficiency Measurement

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William greene stern school of business new york university l.jpg

William Greene

Stern School of Business

New York University

Efficiency Measurement


Lab session 2 l.jpg

Lab Session 2

Stochastic Frontier Estimation


Application to spanish dairy farms l.jpg

Application to Spanish Dairy Farms

N = 247 farms, T = 6 years (1993-1998)


Using farm means of the data l.jpg

Using Farm Means of the Data


Ols vs frontier mle l.jpg

OLS vs. Frontier/MLE


Jlms inefficiency estimator l.jpg

JLMS Inefficiency Estimator

FRONTIER ; LHS = the variable

; RHS = ONE, the variables

; EFF = the new variable $

Creates a new variable in the data set.

FRONTIER ; LHS = YIT ; RHS = X ; EFF = U_i $

Use ;Techeff = variable to compute exp(-u).


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Confidence Intervals for Technical Inefficiency, u(i)


Slide16 l.jpg

Prediction Intervals for Technical Efficiency, Exp[-u(i)]


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Prediction Intervals for Technical Efficiency, Exp[-u(i)]


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Compare SF and DEA


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Similar, but differentwith a crucial pattern


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The Dreaded Error 315 – Wrong Skewness


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Cost Frontier Model


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Linear Homogeneity Restriction


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Translog vs. Cobb Douglas


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Cost Frontier Command

FRONTIER ; COST

; LHS = the variable

; RHS = ONE, the variables

; EFF = the new variable $

ε(i) = v(i) + u(i) [u(i) is still positive]


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Estimated Cost Frontier: C&G


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Cost Frontier Inefficiencies


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Normal-Truncated NormalFrontier Command

FRONTIER [; COST]

; LHS = the variable

; RHS = ONE, the variables

; Model = Truncation

; EFF = the new variable $

ε(i) = v(i) +/- u(i)

u(i) = |U(i)|, U(i) ~ N[μ,2]

The half normal model has μ = 0.


Observations l.jpg

Observations

  • Truncation Model estimation is often unstable

    • Often estimation is not possible

    • When possible, estimates are often wild

  • Estimates of u(i) are usually only moderately affected

  • Estimates of u(i) are fairly stable across models (exponential, truncation, etc.)


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Truncated Normal Model ; Model = T


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Truncated Normal vs. Half Normal


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Multiple Output Cost Function


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Ranking Observations

CREATE ; newname = Rnk ( Variable ) $

Creates the set of ranks. Use in any subsequent analysis.


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